# PAtient-centered mUltidiSciplinary care for vEterans undergoing surgery (PAUSE): a hybrid 1 clinical effectiveness-implementation intervention trial

> **NIH VA I01** · VETERANS ADMIN PALO ALTO HEALTH CARE SYS · 2023 · —

## Abstract

The long-term goal of this project is to improve Veteran health and longevity by developing and
implementing an evidence-based screening strategy to prevent death from second primary lung
cancer (SPLC) in lung cancer survivors. This is important because SPLC is distinct from
recurrence and is one of the main mortality risks for survivors. Despite its importance, there is
no evidence-based standard-of-care for basic questions like which patients to screen for SPLC
and for how long. As treatment and screening improve and the number of lung cancer survivors
increase, there is a critical need for research into interventions to implement evidence-based
cancer screening for cancer survivors. To address this need, we propose to adopt an evidence-
based strategy increasingly used in initial primary lung cancer (IPLC) screening, risk
stratification. Risk stratification has led to marked improvements in IPLC screening, increasing
screening efficiency while also containing ballooning healthcare costs and reducing disparities.
To achieve similar improvements for SPLC, our group has developed and validated a predictive
model to stratify lung cancer survivors by risk of SPLC, now to be piloted in a clinical setting in
this project. Our project is innovative as it is the first (to our knowledge) to implement risk
stratification for SPLC. The objective of our proposal is to develop a Veteran-specific and
disparity-sensitive clinical decision support tool to stratify Veterans by SPLC risk in clinic. Our
central hypotheses are that 1) our model can accurately identify Veterans at high risk for SPLC
and 2) identification of high-risk patients is feasible in a VA clinical setting. To accomplish the
study objective, the following specific aims will be pursued. Aim 1 will validate and adapt our
previously developed SPLC risk prediction model for Veterans, utilizing patient demographics,
smoking history, and IPLC treatment, stage and histology derived from the VA EMR. Aim 2 will
identify factors that influence feasibility and acceptability of a SPLC risk stratification tool, using
a mixed methods approach to focus on provider decision-making and structural or systemic
influences at the facility level. Aim 3 will translate the SPLC risk prediction model into a clinical
decision support tool and pilot the tool at a single VA site, using iterative cycles to improve tool
uptake. Following successful completion of this proposal, the expected research outcomes are
to have 1) an updated optimal SPLC risk prediction model for Veterans and 2) a feasible clinical
decision support tool based on the model, which a clinician can use to identify patients at high
risk of SPLC. In addition, the mentored training program described in this proposal will
accelerate Dr. Julie Wu’s development into an independent health services researcher. Her
mentorship team includes Drs. Arya, Backhus, and Han- leaders in implementation science,
thoracic oncology, and predictive modeling. From this prop...

## Key facts

- **NIH application ID:** 10675316
- **Project number:** 3I01HX003215-02S1
- **Recipient organization:** VETERANS ADMIN PALO ALTO HEALTH CARE SYS
- **Principal Investigator:** Shipra Arya
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2023
- **Award amount:** —
- **Award type:** 3
- **Project period:** 2021-05-01 → 2024-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10675316

## Citation

> US National Institutes of Health, RePORTER application 10675316, PAtient-centered mUltidiSciplinary care for vEterans undergoing surgery (PAUSE): a hybrid 1 clinical effectiveness-implementation intervention trial (3I01HX003215-02S1). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10675316. Licensed CC0.

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